PuSH - Publikationsserver des Helmholtz Zentrums München

Koprulu, M.* ; Smith-Byrne, K.* ; Ferolito, B.R.* ; Macdonald-Dunlop, E.* ; Luan, J.* ; Hedman, K.* ; Ogamba, C.F.* ; Kuliesius, J.* ; Repetto, L.* ; Ramisch, A.* ; Abbasi, F.* ; Johan Ärnlöv,* ; Assimes, T.L.* ; Björck, H.M.* ; Björkander, S.* ; Bøttcher, M.* ; Butterworth, A.S.* ; Chen, Z.M.* ; Cho, K.* ; Clarke, R.J.* ; Cox, S.R.* ; Czene, K.* ; Danesh, J.* ; Dedoussis, G.* ; Elmståhl, S.* ; Eriksson, N.* ; Eriksson, P.* ; Esko, T.* ; Ferreiro-Iglesias, A.* ; Franks, P.W.* ; Fu, J.* ; Gaziano, J.M.* ; Ghanbari, M.* ; Gieger, C. ; Gilly, A. ; Grallert, H. ; Gunter, M.J.* ; Gustafsson, S.* ; Göteson, A.* ; Hall, P.* ; Hansson, O.* ; Harris, S.E.* ; Hayward, C.* ; Herder, C.* ; Hernandez-Pacheco, N.* ; Hijazi, Z.* ; Hillary, R.F.* ; Hopewell, J.C.* ; Hu, S.* ; Hwang, S.* ; Jern, C.* ; Johansson, ?.* ; Jonsson, L.* ; Kalnapenkis, A.* ; Kerrison, N.D.* ; Kho, P.F.* ; Klarić, L.* ; Kohleick, L.* ; Kraft, J.* ; Landén, M.* ; Levy, D.* ; Li, L.* ; Lind, L.* ; Long, J.* ; Mattsson, N.* ; Melén, E.* ; Merid, S.K.* ; Mertins, P.* ; Michaëlsson, K.* ; Möller, P.* ; Murgia, F.* ; Nyegaard, M.* ; Park, Y.-C. ; Pearson, E.R.* ; Peters, J.* ; Petrie, J.* ; Png, G. ; Polašek, O.* ; Prins, B.P.* ; Ripke, S.* ; Roden, M.* ; Rohde, P.D.* ; Said, S.* ; Shen, X.* ; Schwenk, J.M.* ; Siegbahn, A.* ; Smith, J.G.* ; Stanne, T.M.* ; Suhre, K.* ; Sundström, J.* ; Thorand, B. ; Valdés‐Márquez, E.* ; Vallerga, C.L.* ; Meurs, J.B.J.v.* ; Viñuela, A.* ; Võsa, U.* ; Wallentin, L.* ; Walters, R.G.* ; Wareham, N.J.* ; Weber, J.E.*

Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome.

Cell, DOI: 10.1016/j.cell.2026.03.049 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Understanding the genetic regulation of circulating protein levels can provide new insights into disease mechanisms. Here, we present the largest proteogenomic study to date (n = 78,664 participants across 38 studies), identifying >24,000 protein quantitative trait loci (QTLs) associated with 1,116 proteins, acting near to (n = 5,040) or distant (n = 19,698) from the cognate gene. Using machine learning-guided effector gene assignment, we provide genetic evidence for pathways, cell types, and tissues that modulate circulating protein levels, highlighting N-linked glycosylation as an important regulatory pathway. We demonstrate that genetic instruments of protein production/function ("cis") versus modulation ("trans") reveal distinct phenotypic insights. We identify proteins as candidates for drug targets and engagement (e.g., plasma furin and cardiovascular diseases) by comparing cis-based genetic evidence with protein-disease associations. Systematic triangulation of trans-protein QTLs (pQTLs) with genetic and protein associations across many diseases highlights potential drug repurposing opportunities, e.g., tyrosine kinase 2 (TYK2) inhibitors for rheumatoid arthritis. Our multi-cohort meta-analyses generate proteogenomic insights into disease mechanisms and new treatment opportunities.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Proteome ; Proteogenomics ; Gene ; Effector ; Phenotype ; Proteomics ; Genetic Screen ; Drug Repositioning
ISSN (print) / ISBN 0092-8674
e-ISSN 1097-4172
Zeitschrift Cell
Verlag Elsevier
Verlagsort Cambridge, Mass.
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
Institute of Translational Genomics (ITG)